Projected distribution shifts and protected area coverage of range-restricted Andean birds under climate change

Avalos, Verónica del Rosario; Hernández, Jaime

doi:10.1016/j.gecco.2015.08.004

Cited by 45 publications

(22 citation statements)

References 55 publications

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“…While our predicted average range losses were similar to those for other regions in the tropics, including Columbia (Velásquez-Tibatá et al 2013), Bolivia and Peru (Avalos and Hernández 2015), it is important to note that unlike those studies, we did not predict that any species will lose their climactically suitable range completely. Further, unlike some other parts of the world, this region of central American has a robust network of protected areas, including the transboundary La Amistad International Park, which forms the core of the species ranges for most of the 48 species in our study (Barrantes 2009).…”

Section: Discussionsupporting

confidence: 77%

Vulnerability of Elevation-Restricted Endemic Birds of the Cordillera de Talamanca (Costa Rica and Panama) to Climate Change

Liu

Sandoval

Sherman

et al. 2020

Preprint

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Animals endemic to tropical mountains are known to be especially vulnerable to climate change. The Cordillera de Talamanca (Costa Rica and Panama) is a geographically isolated mountain chain and global biodiversity hotspot, home to more than 50 endemic bird species. We used eBird community science observations to predict the distributions of a suite of 48 of these endemic birds in 2006-2015, and in 2070, under four climate change scenarios. Species distributions were predicted using program Maxent, incorporating elevation, satellite derived habitat data, and WorldClim climate variables. Model fit, as assessed by Area under the Receiver Operator Curve (AUC) was very high for most species, ranging from 0.877 to 0.992 (mean of 0.94). We found that most species are predicted to undergo range contractions by 2070, with a mean of 15% under modest climate change (RCP 2.6) up to a mean of 40% under more severe climate change (RCP 8.5). Most of the current ranges of these species are within existing protected areas (average of 59% in 2006-2015), and with prospective range contractions, the importance of these protected areas is forecast to increase. We suggest that these predicted range declines should elevate conservation concerns for this suite of species, and vigilance, in the form of better population monitoring, is urgently needed.

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Section: Discussionsupporting

confidence: 77%

Vulnerability of Elevation-Restricted Endemic Birds of the Cordillera de Talamanca (Costa Rica and Panama) to Climate Change

Liu

Sandoval

Sherman

et al. 2020

Preprint

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“…This reinforces the notion that no rule of thumb exists and settings should be model-and sample-specific, which is often ignored in ensemble forecasting approaches that fit all models on the same dataset (e.g. Avalos andHernández 2015, Sales et al 2017).…”

Section: Behold Sample Prevalence Not the Absolute Number Of Backgrosupporting

confidence: 67%

Assessing the reliability of species distribution projections in climate change research

Santini

Benítez‐López

Maiorano

et al. 2020

Preprint

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19Forecasting changes in species distribution under future scenarios is one of the most prolific 20 areas of application for species distribution models (SDMs). However, no consensus yet exists on 21 the reliability of such models for drawing conclusions on species distribution response to changing 22 42 43Keywords 44 Area Under the Curve (AUC), bias, climate change projections, disequilibrium, geographic extent, 45 sample size, Niche modelling, spurious relationships, True Skill Statistics (TSS). 46 47 Understanding how climate shapes species distribution and how range shifts may be driven by 49 future climatic change is more urgent than ever. In the last thirty years, studies aimed at developing, 50 improving and applying species distribution models (SDMs) have proliferated (Araújo et al. 2019), 51 and forecasting changes in species distribution under future scenarios is one of the most popular 52 areas of application for SDMs today (Thuiller et al. 2011, Schloss et al. 2012, Newbold 2018. In 53 SDM-based climate change forecasting studies, models are trained on current data and used to 54 predict the probability of presence under present and future conditions. Models' predictions are 55 often binarized, allowing one to assess whether a species distribution is expected to shift, contract 56 or expand (Newbold 2018). Although many modelling techniques require presence and absence 57 data, many models are fitted using presence-only data, i.e., contrasting presences with random 58 pseudo-absences, or background points, that represent available conditions (Guillera-Arroita et al. 59 2015). The predictive performance of these models is commonly assessed by randomly splitting the 60 dataset into training and testing, and fitting the model on the training dataset and validating it on the 61 testing dataset using discrimination metrics such as the True Skill Statistic (TSS) or the Area Under 62 the Curve (AUC). While several authors have warned about the challenges and uncertainties of 63 projecting future species distribution (Dormann 2007, Peterson et al. 2018), only few studies have 64 tested model performance with empirical data, reporting mixed results (Rapacciuolo et al. 2012, 65

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“…Especially for species with slow colonization-extinction dynamics, SDMs based on occupancy-environment relationships can be expected to produce biased future occupancy patterns (Ovaskainen & Hanski, 2002), and it should be better to base predictions on models that incorporate both rates of local colonization and extinction and their dependence on environmental conditions (Yackulic et al, 2015). Projections of future population development have focused on changes in the distribution patterns (del Rosario Avalos & Hernandez, 2015), while estimates of the future summed occupancies or population sizes have to date received little attention.…”

Section: Introductionmentioning

confidence: 99%

Occupancy versus colonization–extinction models for projecting population trends at different spatial scales

Nordén

Harrison

Mair

et al. 2020

Ecology and Evolution

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Understanding spatiotemporal population trends and their drivers is a key aim in population ecology. We further need to be able to predict how the dynamics and sizes of populations are affected in the long term by changing landscapes and climate. However, predictions of future population trends are sensitive to a range of modeling assumptions. Deadwood‐dependent fungi are an excellent system for testing the performance of different predictive models of sessile species as these species have different rarity and spatial population dynamics, the populations are structured at different spatial scales, and they utilize distinct substrates. We tested how the projected large‐scale occupancies of species with differing landscape‐scale occupancies are affected over the coming century by different modeling assumptions. We compared projections based on occupancy models against colonization–extinction models, conducting the modeling at alternative spatial scales and using fine‐ or coarse‐resolution deadwood data. We also tested effects of key explanatory variables on species occurrence and colonization–extinction dynamics. The hierarchical Bayesian models applied were fitted to an extensive repeated survey of deadwood and fungi at 174 patches. We projected higher occurrence probabilities and more positive trends using the occupancy models compared to the colonization–extinction models, with greater difference for the species with lower occupancy, colonization rate, and colonization:extinction ratio than for the species with higher estimates of these statistics. The magnitude of future increase in occupancy depended strongly on the spatial modeling scale and resource resolution. We encourage using colonization–extinction models over occupancy models, modeling the process at the finest resource‐unit resolution that is utilizable by the species, and conducting projections for the same spatial scale and resource resolution at which the model fitting is conducted. Further, the models applied should include key variables driving the metapopulation dynamics, such as the availability of suitable resource units, habitat quality, and spatial connectivity.

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Projected distribution shifts and protected area coverage of range-restricted Andean birds under climate change

Cited by 45 publications

References 55 publications

Vulnerability of Elevation-Restricted Endemic Birds of the Cordillera de Talamanca (Costa Rica and Panama) to Climate Change

Vulnerability of Elevation-Restricted Endemic Birds of the Cordillera de Talamanca (Costa Rica and Panama) to Climate Change

Assessing the reliability of species distribution projections in climate change research

Occupancy versus colonization–extinction models for projecting population trends at different spatial scales

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